

import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case				:      int   		=			logging.get_logger(__name__)


__snake_case				:      Optional[Any]   		=			"""https://openaipublic.azureedge.net/jukebox/models/"""
__snake_case				:      Any   		=			{
    """jukebox-1b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """1b_lyrics/prior_level_2.pth.tar""",
    ],
    """jukebox-5b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """5b_lyrics/prior_level_2.pth.tar""",
    ],
}


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if key.endswith(""".model.1.bias""") and len(key.split(""".""")) > 1_0:
						a_						:						Dict 							= key.replace(""".model.1.bias"""  ,	""".conv1d_1.bias""")
			elif key.endswith(""".model.1.weight""") and len(key.split(""".""")) > 1_0:
						a_						:						int 							= key.replace(""".model.1.weight"""  ,	""".conv1d_1.weight""")
			elif key.endswith(""".model.3.bias""") and len(key.split(""".""")) > 1_0:
						a_						:						Union[str, Any] 							= key.replace(""".model.3.bias"""  ,	""".conv1d_2.bias""")
			elif key.endswith(""".model.3.weight""") and len(key.split(""".""")) > 1_0:
						a_						:						str 							= key.replace(""".model.3.weight"""  ,	""".conv1d_2.weight""")

			if "conditioner_blocks.0." in key:
						a_						:						int 							= key.replace("""conditioner_blocks.0"""  ,	"""conditioner_blocks""")

			if "prime_prior" in key:
						a_						:						List[Any] 							= key.replace("""prime_prior"""  ,	"""encoder""")

			if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
						a_						:						Optional[int] 							= key.replace(""".emb."""  ,	""".""")

			if key.endswith("""k"""):  # replace vqvae.X.k with vqvae.X.codebook
						return key.replace(""".k"""  ,	""".codebook""")
			if "y_emb." in key:
						return key.replace("""y_emb."""  ,	"""metadata_embedding.""")

			if "x_emb.emb." in key:
						a_						:						Optional[int] 							= key.replace("""0.x_emb.emb"""  ,	"""embed_tokens""")

			if "prime_state_ln" in key:
						return key.replace("""prime_state_ln"""  ,	"""encoder.final_layer_norm""")
			if ".ln" in key:
						return key.replace(""".ln"""  ,	""".layer_norm""")
			if "_ln" in key:
						return key.replace("""_ln"""  ,	"""_layer_norm""")

			if "prime_state_proj" in key:
						return key.replace("""prime_state_proj"""  ,	"""encoder.proj_in""")
			if "prime_x_out" in key:
						return key.replace("""prime_x_out"""  ,	"""encoder.lm_head""")
			if "prior.x_out" in key:
						return key.replace("""x_out"""  ,	"""fc_proj_out""")
			if "x_emb" in key:
						return key.replace("""x_emb"""  ,	"""embed_tokens""")

			return key


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						Dict 							= {}
			import re

			a_						:						Optional[int] 							= re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""")
			a_						:						str 							= re.compile(
			    r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""")
			a_						:						List[Any] 							= re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)""")

			a_						:						int 							= re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""")
			a_						:						Union[str, Any] 							= re.compile(
			    r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""")
			a_						:						Tuple 							= re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)""")

			a_						:						Optional[Any] 							= re.compile(r"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)""")
			a_						:						Any 							= re.compile(
			    r"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""")
			a_						:						Dict 							= re.compile(r"""conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)""")

			for original_key, value in state_dict.items():
						# rename vqvae.encoder keys
						if re_encoder_block_conv_in.fullmatch(a_):
									a_						:						Optional[Any] 							= re_encoder_block_conv_in.match(a_)
									a_						:						Optional[int] 							= regex_match.groups()
									a_						:						Dict 							= int(groups[2]) * 2 + int(groups[3])
									a_						:						Optional[int] 							= f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
									a_						:						List[Any] 							= re_encoder_block_conv_in.sub(a_  ,	a_)

						elif re_encoder_block_resnet.fullmatch(a_):
									a_						:						Optional[int] 							= re_encoder_block_resnet.match(a_)
									a_						:						str 							= regex_match.groups()
									a_						:						Union[str, Any] 							= int(groups[2]) * 2 + int(groups[3])
									a_						:						Optional[int] 							= {"1": 1, "3": 2}[groups[-2]]
									a_						:						Optional[int] 							= f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
									a_						:						Optional[Any] 							= f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
									a_						:						Union[str, Any] 							= prefix + resnet_block
									a_						:						int 							= re_encoder_block_resnet.sub(a_  ,	a_)

						elif re_encoder_block_proj_out.fullmatch(a_):
									a_						:						Optional[int] 							= re_encoder_block_proj_out.match(a_)
									a_						:						Any 							= regex_match.groups()
									a_						:						Optional[Any] 							= f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
									a_						:						Optional[int] 							= re_encoder_block_proj_out.sub(a_  ,	a_)

						# rename vqvae.decoder keys
						elif re_decoder_block_conv_out.fullmatch(a_):
									a_						:						List[Any] 							= re_decoder_block_conv_out.match(a_)
									a_						:						int 							= regex_match.groups()
									a_						:						Union[str, Any] 							= int(groups[2]) * 2 + int(groups[3]) - 2
									a_						:						Optional[int] 							= f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
									a_						:						str 							= re_decoder_block_conv_out.sub(a_  ,	a_)

						elif re_decoder_block_resnet.fullmatch(a_):
									a_						:						Any 							= re_decoder_block_resnet.match(a_)
									a_						:						Optional[int] 							= regex_match.groups()
									a_						:						List[str] 							= int(groups[2]) * 2 + int(groups[3]) - 2
									a_						:						str 							= {"1": 1, "3": 2}[groups[-2]]
									a_						:						Any 							= f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
									a_						:						Union[str, Any] 							= f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
									a_						:						Union[str, Any] 							= prefix + resnet_block
									a_						:						Optional[int] 							= re_decoder_block_resnet.sub(a_  ,	a_)

						elif re_decoder_block_proj_in.fullmatch(a_):
									a_						:						Union[str, Any] 							= re_decoder_block_proj_in.match(a_)
									a_						:						str 							= regex_match.groups()
									a_						:						Optional[Any] 							= f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
									a_						:						List[Any] 							= re_decoder_block_proj_in.sub(a_  ,	a_)

						# rename prior cond.model to upsampler.upsample_block and resnet
						elif re_prior_cond_conv_out.fullmatch(a_):
									a_						:						Tuple 							= re_prior_cond_conv_out.match(a_)
									a_						:						Any 							= regex_match.groups()
									a_						:						Dict 							= int(groups[1]) * 2 + int(groups[2]) - 2
									a_						:						Optional[int] 							= f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
									a_						:						Any 							= re_prior_cond_conv_out.sub(a_  ,	a_)

						elif re_prior_cond_resnet.fullmatch(a_):
									a_						:						Optional[int] 							= re_prior_cond_resnet.match(a_)
									a_						:						List[str] 							= regex_match.groups()
									a_						:						List[Any] 							= int(groups[1]) * 2 + int(groups[2]) - 2
									a_						:						Optional[int] 							= {"1": 1, "3": 2}[groups[-2]]
									a_						:						Tuple 							= f'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
									a_						:						List[Any] 							= f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
									a_						:						Dict 							= prefix + resnet_block
									a_						:						Optional[int] 							= re_prior_cond_resnet.sub(a_  ,	a_)

						elif re_prior_cond_proj_in.fullmatch(a_):
									a_						:						Optional[Any] 							= re_prior_cond_proj_in.match(a_)
									a_						:						Optional[Any] 							= regex_match.groups()
									a_						:						Tuple 							= f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
									a_						:						Optional[Any] 							= re_prior_cond_proj_in.sub(a_  ,	a_)

						# keep original key
						else:
									a_						:						List[str] 							= original_key

						a_						:						List[str] 							= replace_key(a_)

						if f'''{key_prefix}.{key}''' not in model_state_dict or key is None:
									print(f'''failed converting {original_key} to {key}, does not match''')

						# handle missmatched shape
						elif value.shape != model_state_dict[f'''{key_prefix}.{key}'''].shape:
									a_						:						int 							= model_state_dict[f'''{key_prefix}.{key}''']
									print(f'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match''')
									a_						:						int 							= original_key

						a_						:						List[str] 							= original_key
						a_						:						Optional[Any] 							= value

			return new_dict


@torch.no_grad()
def 					_UpperCAmelCase				(						a__=None  ,	a__=None):

			'''simple docstring'''



			for file in MODEL_MAPPING[model_name]:
						if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/")[-1]}'''):
									a_						:						Tuple 							= requests.get(f'''{PREFIX}{file}'''  ,	allow_redirects=a_)
									os.makedirs(f'''{pytorch_dump_folder_path}/'''  ,	exist_ok=a_)
									open(f'''{pytorch_dump_folder_path}/{file.split("/")[-1]}'''  ,	"""wb""").write(r.content)

			a_						:						List[Any] 							= MODEL_MAPPING[model_name.split("""/""")[-1]]

			a_						:						Dict 							= JukeboxConfig.from_pretrained(a_)
			a_						:						Union[str, Any] 							= JukeboxModel(a_)

			a_						:						Dict 							= []
			a_						:						Optional[int] 							= {}
			for i, dict_name in enumerate(a_):
						a_						:						Optional[Any] 							= torch.load(f'''{pytorch_dump_folder_path}/{dict_name.split("/")[-1]}''')["model"]

						a_						:						Tuple 							= {}
						for k in old_dic.keys():
									if k.endswith(""".b"""):
												a_						:						Any 							= old_dic[k]
									elif k.endswith(""".w"""):
												a_						:						Dict 							= old_dic[k]
									elif "level_2" not in dict_name and "cond.model." in k:
												a_						:						Optional[Any] 							= old_dic[k]
									else:
												a_						:						Union[str, Any] 							= old_dic[k]

						a_						:						Union[str, Any] 							= "vqvae" if i == 0 else f'''priors.{3 - i}'''
						a_						:						List[Any] 							= fix_jukebox_keys(a_  ,	model.state_dict()  ,	a_  ,	a_)
						weight_dict.append(a_)

			a_						:						int 							= weight_dict.pop(0)
			model.vqvae.load_state_dict(a_)
			for i in range(len(a_)):
						model.priors[i].load_state_dict(weight_dict[2 - i])

			Path(a_).mkdir(exist_ok=a_)
			with open(f'''{pytorch_dump_folder_path}/mapping.json'''  ,	"""w""") as txtfile:
						json.dump(a_  ,	a_)

			print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''')
			model.save_pretrained(a_)

			return weight_dict


if __name__ == "__main__":
	__snake_case				:      List[Any]   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""jukebox-5b-lyrics""",
	    type=str,
	    help="""Name of the model you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default="""jukebox-5b-lyrics-converted""",
	    type=str,
	    help="""Path to the output PyTorch model directory.""",
	)
	__snake_case				:      str   		=			parser.parse_args()
	convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)









'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   []






def   __A    (				a_    :	list[list[int]]   ,a_    :	int   ,a_    :	int						):
		for i in range(len(a_						)						):
				if board[row][i] == 1:
						return False
		for i in range(len(a_						)						):
				if board[i][column] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,-1   ,-1						)						):
				if board[i][j] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,len(a_						)						)						):
				if board[i][j] == 1:
						return False
		return True






def   __A    (				a_    :	list[list[int]]   ,a_    :	int						):
		if row >= len(a_						):
				solution.append(a_						)
				printboard(a_						)
				print()
				return True
		for i in range(len(a_						)						):
				if is_safe(a_   ,a_   ,a_						):
						lowerCAmelCase  :     Dict       						= 1
						solve(a_   ,row + 1						)
						lowerCAmelCase  :     Optional[int]       						= 0
		return False






def   __A    (				a_    :	list[list[int]]						):
		for i in range(len(a_						)						):
				for j in range(len(a_						)						):
						if board[i][j] == 1:
								print("Q"   ,end=" "						)
						else:
								print("."   ,end=" "						)
				print()


# n=int(input("The no. of queens"))
lowerCAmelCase									=   8
lowerCAmelCase									=   [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print("""The total no. of solutions are :""", len(solution))



